Natural Language Processing Fundamentals For Developers

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Natural Language Processing Fundamentals For Developers

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Author : Oswald Campesato
language : en
Publisher: Mercury Learning and Information
Release Date : 2021-06-14

Natural Language Processing Fundamentals For Developers written by Oswald Campesato and has been published by Mercury Learning and Information this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-14 with Computers categories.

This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA (“state of the art”). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included. FEATURES: Covers extensive topics related to natural language processing Includes separate appendices on regular expressions and probability/statistics Features companion files with source code and figures from the book. The companion files are available online by emailing the publisher with proof of purchase at [email protected].

Natural Language Processing Fundamentals

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Author : Sohom Ghosh
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-30

Natural Language Processing Fundamentals written by Sohom Ghosh and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-30 with Computers categories.

Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems. Key FeaturesAssimilate key NLP concepts and terminologies Explore popular NLP tools and techniquesGain practical experience using NLP in application codeBook Description If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language. What you will learnObtain, verify, and clean data before transforming it into a correct format for usePerform data analysis and machine learning tasks using PythonUnderstand the basics of computational linguisticsBuild models for general natural language processing tasksEvaluate the performance of a model with the right metricsVisualize, quantify, and perform exploratory analysis from any text dataWho this book is for Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It'll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.

Natural Language Processing With Python Quick Start Guide

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Author : Nirant Kasliwal
language : en
Publisher:
Release Date : 2018-11-30

Natural Language Processing With Python Quick Start Guide written by Nirant Kasliwal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-30 with Computers categories.

Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key Features A no-math, code-driven programmer's guide to text processing and NLP Get state of the art results with modern tooling across linguistics, text vectors and machine learning Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch Book Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learn Understand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpus Work with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clustering Deep Learning in NLP using PyTorch with a code-driven introduction to PyTorch Using an NLP project management Framework for estimating timelines and organizing your project into stages Hack and build a simple chatbot application in 30 minutes Deploy an NLP or machine learning application using Flask as RESTFUL APIs Who this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.

Practical Natural Language Processing

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Author : Sowmya Vajjala
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-17

Practical Natural Language Processing written by Sowmya Vajjala and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-17 with Computers categories.

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective

Deep Learning For Coders With Fastai And Pytorch

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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29

Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Deep Learning For Natural Language Processing

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Author : Palash Goyal
language : en
Publisher: Apress
Release Date : 2018-06-26

Deep Learning For Natural Language Processing written by Palash Goyal and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-26 with Computers categories.

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.

Speech And Language Processing

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Author : Daniel Jurafsky
language : en
Publisher:
Release Date : 2000-01

Speech And Language Processing written by Daniel Jurafsky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01 with Automatic speech recognition categories.

This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

Mastering Natural Language Processing

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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :

Mastering Natural Language Processing written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Unveil the Secrets of Language Understanding and Generation In the realm of artificial intelligence and communication, Natural Language Processing (NLP) stands as a transformative force that bridges the gap between humans and machines. "Mastering Natural Language Processing" is your definitive guide to comprehending and harnessing the potential of this dynamic field, empowering you to create intelligent language-based applications with precision. About the Book: As technology evolves, the ability to understand and generate human language becomes increasingly essential. "Mastering Natural Language Processing" offers a comprehensive exploration of NLP—a crucial discipline in the world of AI and communication. This book caters to both beginners and experienced learners aiming to excel in NLP concepts, techniques, and applications. Key Features: NLP Fundamentals: Begin by understanding the core principles of Natural Language Processing. Learn about linguistic concepts, tokenization, and language models. Text Classification and Sentiment Analysis: Dive into text analysis techniques. Explore methods for classifying text and determining sentiment, enabling you to understand user opinions and emotions. Named Entity Recognition: Grasp the art of identifying entities in text. Understand how to extract names, places, dates, and other crucial information from unstructured data. Language Generation: Explore techniques for generating human-like language. Learn how to create chatbots, language models, and automated content. Machine Translation: Understand the significance of machine translation. Learn how to build systems that translate text between languages with accuracy. Speech Recognition: Delve into the realm of speech recognition. Explore techniques for converting spoken language into text, enabling voice interfaces and transcription. Question Answering Systems: Grasp the power of question-answering systems. Learn how to build applications that provide answers to user questions based on available data. Real-World Applications: Gain insights into how NLP is applied across industries. From customer service to healthcare, discover the diverse applications of natural language processing. Why This Book Matters: In an age of communication and interaction, mastering NLP offers a competitive advantage. "Mastering Natural Language Processing" empowers data scientists, developers, and technology enthusiasts to leverage NLP concepts, enabling them to create intelligent language-based applications that enhance user experiences and drive innovation. Revolutionize Communication with AI: In the landscape of artificial intelligence, Natural Language Processing is transforming how humans and machines interact. "Mastering Natural Language Processing" equips you with the knowledge needed to leverage NLP concepts, enabling you to create intelligent language-based applications that bridge communication gaps and redefine possibilities. Whether you're a seasoned practitioner or new to the world of NLP, this book will guide you in building a solid foundation for effective language-based solutions. Your journey to mastering Natural Language Processing starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Linguistic Fundamentals For Natural Language Processing

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Author : Emily M. Bender
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2013-06-01

Linguistic Fundamentals For Natural Language Processing written by Emily M. Bender and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-01 with Computers categories.

Many NLP tasks have at their core a subtask of extracting the dependencies—who did what to whom—from natural language sentences. This task can be understood as the inverse of the problem solved in different ways by diverse human languages, namely, how to indicate the relationship between different parts of a sentence. Understanding how languages solve the problem can be extremely useful in both feature design and error analysis in the application of machine learning to NLP. Likewise, understanding cross-linguistic variation can be important for the design of MT systems and other multilingual applications. The purpose of this book is to present in a succinct and accessible fashion information about the morphological and syntactic structure of human languages that can be useful in creating more linguistically sophisticated, more language-independent, and thus more successful NLP systems. Table of Contents: Acknowledgments / Introduction/motivation / Morphology: Introduction / Morphophonology / Morphosyntax / Syntax: Introduction / Parts of speech / Heads, arguments, and adjuncts / Argument types and grammatical functions / Mismatches between syntactic position and semantic roles / Resources / Bibliography / Author's Biography / General Index / Index of Languages

Introduction To Natural Language Processing

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Author : Jacob Eisenstein
language : en
Publisher: MIT Press
Release Date : 2019-10-01

Introduction To Natural Language Processing written by Jacob Eisenstein and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-01 with Computers categories.

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.